Laser Speckle Imaging (LSI) may be used to image blood flow and tissue perfusion.
During LSI, the tissue is illuminated using coherent light (e.g., from a laser source), and a speckle image of the tissue is typically acquired using a monochrome image sensor (e.g., CCD or CMOS) with a well-defined exposure time. Due to the coherence of the light used in such imaging, the recorded image contains a speckle pattern. The optical system maps the speckle pattern to the picture elements (pixels) of the image sensor in such a way that each pixel of the image sensor samples a small number of speckles or it oversamples by having a few pixels sampling a single speckle. Typically, near-infrared (NIR) light is used for the illumination due to the reduced opacity of the tissue at these wavelengths. During blood cell movement associated with tissue perfusion, the speckle pattern changes continuously. The exposure time is set such that the speckle pattern changes faster than the exposure time, and thus, the changing speckle pattern becomes blurred. In a spatial-domain approach, the recorded speckle image(s) may be analyzed for contrast by calculating the standard deviation and mean in a kernel around each pixel. In the case of non-perfused tissue (i.e., tissue in which no red blood cells are moving), the speckle pattern has a high contrast because no motion occurs to blur speckles. By applying a non-linear function to each pixel, the contrast image can be subsequently converted into a map of the perfusion state of the tissue. In a time-domain approach, the recorded speckle image(s) may be analyzed for contrast by calculating the standard deviation and mean in a series of image frames for the same pixel. The spatial-domain and time-domain approaches may also be combined. In such a combined approach, the recorded speckle image(s) may be analyzed for contrast by calculating the standard deviation and mean of a series of image frames in a kernel around each pixel.
As an alternative to monochrome image sensors, color image sensors may be used to create monochrome images. Color image sensors may be built using, for example, a Bayer pattern filter in which four pixels forming a square array have one red pixel, two green pixels, and one blue pixel. The acquired raw pixel data filtered through the Bayer pattern may be first converted into a color image using a so-called de-Bayering or demosaicing conversion, and the resulting color image may be subsequently converted into a grayscale/monochrome image. The conversion of the color image to a monochrome image is typically performed by averaging the RGB colors that result from the de-Bayering conversion, sometimes as a weighted average. Although these discrete steps can be combined in a single step, a single pixel in the resulting monochrome image is based on multiple pixels from the color sensor (usually some form of averaging of pixels of the image sensor).
While this conversion of a color image to monochrome image is acceptable for most imaging systems, and often results in reduced noise, such an approach has a negative effect in LSI applications. In LSI, the contrast of the monochrome (speckle) image within a small area may be used to determine perfusion in the tissue. The averaging of multiple pixels when converting a color image to a monochrome speckle image may reduce the contrast and, consequently, reduce the dynamic range of the LSI system and the speckle image quality. The maximum contrast may be reduced, and, thus, a completely static object/non-perfused area of tissue may exhibit a lower contrast than that attainable with a pure monochrome sensor.
Furthermore, in a Bayer pattern color image sensor, although all pixels may be sensitive to near-infrared, this sensitivity is typically not equal for the different color pixels. Therefore, the use of such a color sensor in a system with near-infrared illumination presents an issue because the different sensitivities result in a pattern on the image. Because spatial-domain (or combined time- and spatial-domain) LSI analyzes the contrast within a kernel of several pixels, this pattern may cause an error in the perfusion measurement.
In one approach to addressing this problem, red laser illumination and solely the red pixels of a color sensor can be used to produce a speckle image. However, using only the red pixels of the color sensor to produce the perfusion image limits the utilization of the sensor pixels to only one quarter, which contributes to a reduced resolution of the resultant image. Furthermore, red illumination penetrates less deeply into the tissue compared to near-infrared illumination, and it is not at the isosbestic point of oxy- and deoxyhaemoglobin.
Another drawback of current technologies is that in clinical applications, the speckle image alone lacks contextual information and is noisy due to the speckles; thus, clinical assessment may require a color image. Therefore, to perform clinical assessment, usually a speckle image is linked to a white light image from the same imaging area in order to correlate the perfusion to the corresponding area of the tissue. Currently available technologies either do not produce such a white light image at all, or produce it with a separate image sensor, which may in some instances have the disadvantage of requiring a more complex optical system.
Another drawback of current technologies is the reduction of speckle contrast by ambient light. In LSI, the detection of light other than light from the coherent source may reduce the speckle contrast. This in turn may reduce the quality of the perfusion image.
It is desirable for LSI systems to possess the color image data processing capabilities which maximize the contrast to more accurately represent perfusion, to effectively present speckle images along with white light imaging to the clinician to aid in clinical assessment, and to detect, reduce or eliminate, and/or correct for ambient light.